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Record W2494301514 · doi:10.1109/tmtt.2016.2586486

System for Bulk Dielectric Permittivity Estimation of Breast Tissues at Microwave Frequencies

2016· article· en· W2494301514 on OpenAlexafffund
Jeremie Bourqui, Elise Fear

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Calgary
FundersAlberta Innovates - Health SolutionsAlberta Innovates - Technology Futures
KeywordsPermittivityMicrowaveMaterials scienceDielectricWidebandAcousticsTime domainMicrowave imagingElectronic engineeringRelative permittivityOpticsOptoelectronicsComputer scienceTelecommunicationsPhysicsEngineering

Abstract

fetched live from OpenAlex

This paper presents an apparatus capable of measuring the average dielectric properties of the human breast. It is composed of two arrays between which the breast is placed. Each array is fitted with five ultra-wideband (UWB) sensors operating from 1.5 to 10 GHz. Direct contact is made between the breast skin and the sensor arrays, avoiding any matching liquid. The transmission coefficients for all possible sensor pairs in the two arrays are measured in 15 s. The data are then transformed to the time domain, and a time delay spectroscopy technique is used to estimate permittivity. The system was tested using phantoms with known properties and showed accurate estimations. Tests on human subjects resulted in dielectric property estimates in line with published data, while variation between scans of the same volunteer is as small as 2%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.218
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations85
Published2016
Admission routes2
Has abstractyes

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